{"title":"Causal Inference of Social Experiments using Orthogonal Designs.","authors":"James J Heckman, Rodrigo Pinto","doi":"10.1007/s40953-022-00307-w","DOIUrl":null,"url":null,"abstract":"<p><p>Orthogonal Arrays are a powerful class of experimental designs that has been widely used to determine efficient arrangements of treatment factors in randomized controlled trials. Despite its popularity, the method is seldom used in social sciences. Social experiments must cope with randomization compromises such as noncompliance that often prevents the use of elaborate designs. We present a novel application of orthogonal designs that addresses the particular challenges arising in social experiments. We characterize the identification of counterfactual variables as a finite mixture problem in which choice incentives, rather than treatment factors, are randomly assigned. We show that the causal inference generated by an orthogonal array of incentives greatly outperforms a traditional design.</p>","PeriodicalId":42219,"journal":{"name":"JOURNAL OF QUANTITATIVE ECONOMICS","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854338/pdf/nihms-1856433.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOURNAL OF QUANTITATIVE ECONOMICS","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s40953-022-00307-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/9/12 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Orthogonal Arrays are a powerful class of experimental designs that has been widely used to determine efficient arrangements of treatment factors in randomized controlled trials. Despite its popularity, the method is seldom used in social sciences. Social experiments must cope with randomization compromises such as noncompliance that often prevents the use of elaborate designs. We present a novel application of orthogonal designs that addresses the particular challenges arising in social experiments. We characterize the identification of counterfactual variables as a finite mixture problem in which choice incentives, rather than treatment factors, are randomly assigned. We show that the causal inference generated by an orthogonal array of incentives greatly outperforms a traditional design.
期刊介绍:
The Journal of Quantitative Economics (JQEC) is a refereed journal of the Indian Econometric Society (TIES). It solicits quantitative papers with basic or applied research orientation in all sub-fields of Economics that employ rigorous theoretical, empirical and experimental methods. The Journal also encourages Short Papers and Review Articles. Innovative and fundamental papers that focus on various facets of Economics of the Emerging Market and Developing Economies are particularly welcome. With the help of an international Editorial board and carefully selected referees, it aims to minimize the time taken to complete the review process while preserving the quality of the articles published.